Many textual documents have been made available over the internet in the past 10-20 , years. Search engines are capable of indexing these textual documents based on the words that they contain. People can find a desired relevant topic with a keyword search using a search engine after the indexing has been performed. Thus, textual documents are fairly accessible over the internet.
More recently, especially in the last 5-10 , years, images are increasingly being made available over the internet. For example, images, including videos, are being uploaded to websites that enable the images to be viewed and otherwise shared. It is already difficult to locate a video that addresses a desired topic, and the rate at which images are being added to the internet is increasing. In contrast with textual documents, videos often do not include a sufficiently representative set of textual words, if they include any. Consequently, it is difficult for current search engines to index or otherwise organize the vast collection of videos on the internet.
One approach to organizing videos is to annotate each video with one or more concepts. The annotated concepts can then be indexed for subsequent searching and retrieval of the associated videos. This annotation can be performed manually by people that view each video. However, manual approaches to annotating videos are time-consuming, financially untenable, and prone to inconsistencies resulting from viewers' subjectivities. Automated approaches have also been developed. These automated approaches can be significantly more efficient than manual ones and can be scaled accordingly. Unfortunately, current automated approaches to annotating videos produce many mislabeled concepts.